Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Speech recognition in noisy environments
Speech recognition in noisy environments
Speech recognition in mobile environments
Speech recognition in mobile environments
Investigations on inter-speaker variability in the feature space
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
| Hi-index | 0.08 |
A solution to the problem of speech recognition with signals corrupted by coders is presented. The coding-decoding distortion is modelled as feature dependent. This model is employed to propose an unsupervised expectation-maximization (EM) estimation algorithm of the coding-decoding distortion that is able to cancel the effect of coders with as few as one adapting utterance. No knowledge about the coder is required. The feature-dependent adaptation can give a word error rate (WER) 21% lower than the feature-independent model. Finally, when compared to the baseline system, the reduction in WER can be as high as 70%.